Abstract

BackgroundMetabolomics represents a powerful tool for exploring modulation of the human metabolome in response to food intake. However, the choice of multivariate statistical approach is not always evident, especially for complex experimental designs with repeated measurements per individual. Here we have investigated the serum metabolic responses to two breakfast meals: an egg and ham based breakfast and a cereal based breakfast using three different multivariate approaches based on the Projections to Latent Structures framework.MethodsIn a cross over design, 24 healthy volunteers ate the egg and ham breakfast and cereal breakfast on four occasions each. Postprandial serum samples were subjected to metabolite profiling using 1H nuclear magnetic resonance spectroscopy and metabolites were identified using 2D nuclear magnetic resonance spectroscopy. Metabolic profiles were analyzed using Orthogonal Projections to Latent Structures with Discriminant Analysis and Effect Projections and ANOVA-decomposed Projections to Latent Structures.ResultsThe Orthogonal Projections to Latent Structures with Discriminant Analysis model correctly classified 92 and 90% of the samples from the cereal breakfast and egg and ham breakfast, respectively, but confounded dietary effects with inter-personal variability. Orthogonal Projections to Latent Structures with Effect Projections removed inter-personal variability and performed perfect classification between breakfasts, however at the expense of comparing means of respective breakfasts instead of all samples. ANOVA-decomposed Projections to Latent Structures managed to remove inter-personal variability and predicted 99% of all individual samples correctly. Proline, tyrosine, and N-acetylated amino acids were found in higher concentration after consumption of the cereal breakfast while creatine, methanol, and isoleucine were found in higher concentration after the egg and ham breakfast.ConclusionsOur results demonstrate that the choice of statistical method will influence the results and adequate methods need to be employed to manage sample dependency and repeated measurements in cross-over studies. In addition, 1H nuclear magnetic resonance serum metabolomics could reproducibly characterize postprandial metabolic profiles and identify discriminatory metabolites largely reflecting dietary composition.Trial registrationRegistered with ClinicalTrials.gov, identifier: NCT02039596. Date of registration: January 17, 2014.

Highlights

  • Metabolomics represents a powerful tool for exploring modulation of the human metabolome in response to food intake

  • The OPLS-DA model correctly classified 92% of the postprandial samples from the cereal breakfast (CB) and 90% of the postprandial samples from the egg and ham breakfast (EHB) while the ANalysis Of Variance (ANOVA)-projections to latent structures (PLS) correctly classified 99% of samples with respect to breakfast type (Table 3)

  • The ability of the OPLS-DA, OPLS-Effect Projections (EP), and ANOVAPLS models to discriminate between metabolic profiles of postprandial samples from volunteers who had consumed the CB and the EHB are displayed in Figs. 3 and 4

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Summary

Introduction

Metabolomics represents a powerful tool for exploring modulation of the human metabolome in response to food intake. To establish associations and causation between diet and health, objective and reliable methods are needed to measure dietary exposure [1]. Subjective assessment methods are commonly used and these include dietary records, food diaries, 24-h dietary recalls, food frequency questionnaires and diet history records. These methods rely on subjects’ own reports of their diets [2]. Providing accurate and reliable measurements of dietary exposure constitutes one of the most challenging problems in nutrition research today [7]

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